·         Table of Contents (Volume #5, Issue #4)

·         Cover Page (Volume #5, Issue #4)

·         Editorial (Volume #5, Issue #4)

Paper #1

Process Discovery Enhancement with Trace Clustering and Profiling

Muhammad Faizan, Megat F. Zuhairi and Shahrinaz Ismail

Abstract: The potential in process mining is progressively growing due to the increasing amount of event-data. Process mining strategies use event-logs to automatically classify process models, recommend improvements, predict processing times, check conformance, and recognize anomalies/deviations and bottlenecks. However, proper handling of event-logs while evaluating and using them as input is crucial to any process mining technique. When process mining techniques are applied to flexible systems with a large number of decisions to take at runtime, the outcome is often unstructured or semi-structured process models that are hard to comprehend. Existing approaches are good at discovering and visualizing structured processes but often struggle with less structured ones. Surprisingly, process mining is most useful in domains where flexibility is desired. A good illustration is the "patient treatment" process in a hospital, where the ability to deviate from dealing with changing conditions is crucial. It is useful to have insights into actual operations. However, there is a significant amount of diversity, which contributes to complicated, difficult-to-understand models. Trace clustering is a method for decreasing the complexity of process models in this context while also increasing their comprehensibility and accuracy. This paper discusses process mining, event-logs, and presenting a clustering approach to pre-process event-logs, i.e., a homogeneous subset of the event-log is created. A process model is generated for each subset. These homogeneous subsets are then evaluated independently from each other, which significantly improving the quality of mining results in flexible environments. The presented approach improves the fitness and precision of a discovered model while reducing its complexity, resulting in well-structured and easily understandable process discovery results.

Keywords: Incremental trace clustering; Process mining; Pre-processing; Process discovery; Trace profiling.

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Paper #2

Towards 76-81 GHz Scalable Phase Shifting by Folded Dual-strip Shielded Coplanar Waveguide with Liquid Crystals

Jinfeng Li

Abstract: Unconventional folded shielded coplanar waveguide (FS-CPW) has yet to be fully investigated for tunable dielectrics-based applications. This work formulates designs of FS-CPW based on liquid crystals (LC) for electrically controlled 0-360° phase shifters, featuring a minimally redundant approach for reducing the LC volume and hence the costs for mass production. The design exhibits a few conceptual features that make it stand apart from others, noteworthy, the dual-strip structure with a simplified enclosure engraved that enables LC volume sharing between adjacent core lines. Insertion loss reduction by 0.77 dB and LC volume reduction by 1.62% per device are reported at 77 GHz, as compared with those of the conventional single-strip configuration. Based on the proof-of-concept results obtained for the novel dual-strip FS-CPW proposed, this work provides a springboard for follow-up investible propositions that will underpin the development of a phased array demonstrator.

Keywords: computational electromagnetics; liquid crystal; meandered coplanar waveguide; passive microwave components; phase shifter; shielded coplanar waveguide; wireless communication; 77 GHz.

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Paper #3

An Intelligent License Plate Detection and Recognition Model Using Deep Neural Networks

J.Andrew Onesimu, Robin D.Sebastian, Yuichi Sei and Lenny Christopher

Abstract: One of the largest automotive sectors in the world is India. The number of vehicles traveling by road has increased in recent times. In malls or other crowded places, many vehicles enter and exit the parking area. Due to the increase in vehicles, it is difficult to manually note down the license plate number of all the vehicles passing in and out of the parking area. Hence, it is necessary to develop an Automatic License Plate Detection and Recognition (ALPDR) model that recognize the license plate number of vehicles automatically. To automate this process, we propose a three-step process that will detect the license plate, segment the characters and recognize the characters present in it. Detection is done by converting the input image to a bi-level image. Using region props the characters are segmented from the detected license plate. A two-layer CNN model is developed to recognize the segmented characters. The proposed model automatically updates the details of the car entering and exiting the parking area to the database. The proposed ALPDR model has been tested in several conditions such as blurred images, different distances from the cameras, day and night conditions on the stationary vehicles. Experimental result shows that the proposed system achieves 91.1%, 96.7%, and 98.8% accuracy on license plate detection, segmentation, and recognition respectively which is superior to state-of-the-art literature models.

Keywords: car license plate recognition; convolutional neural network; deep learning; deep neural networks; license plate detection.

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Paper #4

A Novel Hybrid Signal Decomposition Technique for Transfer Learning Based Industrial Fault Diagnosis

Zurana Mehrin Ruhi, Sigma Jahan and Jia Uddin

Abstract: In the fourth industrial revolution, data-driven intelligent fault diagnosis for industrial purposes serves a crucial role. In contemporary times, although deep learning is a popular approach for fault diagnosis, it requires massive amounts of labelled samples for training, which is arduous to come by in the real world. Our contribution to introduce a novel comprehensive intelligent fault detection model using the Case Western Reserve University dataset is divided into two steps. Firstly, a new hybrid signal decomposition methodology is developed comprising Empirical Mode Decomposition and Variational Mode Decomposition to leverage signal information from both processes for effective feature extraction. Secondly, transfer learning with DenseNet121 is employed to alleviate the constraints of deep learning models. Finally, our proposed novel technique surpassed not only previous outcomes but also generated state-of-the-art outcomes represented via the F1 score.

Keywords: Deep learning; Intelligent fault diagnosis; Signal decomposition techniques; Transfer learning.

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Paper #5

Evaluation of Green Alternatives for Blockchain Proof-of-Work (PoW) Approach

Mahdi H. Miraz, Peter S. Excell and Khan Sobayel

Abstract: Following the footprints of Bitcoins, many other cryptocurrencies were developed mostly adopting the same or similar Proof-of-Work (PoW) approach. Since completing the PoW puzzle requires extremely high computing power, consuming a vast amount of electricity, PoW has been strongly criticised for its antithetic stand against the notion of green computing. Use of application-specific hardware, particularly application-specific integrated circuits (ASICs) has further fuelled the debate, as these devices are of no use once they become “legacy” and hence obsolete to compete in the mining race, thus contributing to electronics waste. Therefore, this paper surveys the currently available alternative approaches to PoW and evaluates their applicability - especially their appropriateness in terms of greenness.

Keywords: Application-specific integrated circuit (ASIC); Blockchain; Blockchain mining; Carbon footprint; Green computing; Greenhouse gas emission (GHGE); Information and communication technology (ICT); Proof-of-Stake (PoS); Proof-of-Work (PoW).

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